基于小世界网络的PSO算法在电力系统中的应用

针对发电机的阀点效应,电力系统机组优化组合属于复杂的具有混合型变量的组合优化问题,基于小世界网络(Small World Network,SWN)的邻域模型构造,最优化原理和基本粒子群算法(ParticleSwarm Optimization,PSO),以24个时间段所有机组总耗量最小为目标函数,建立了发电机组优化组合的数学模型.提出了将"平均最短路径小,聚集系数大"的小世界网络邻域结构引入到粒子群算法中,以机组的输出功率作为粒子的位置,给出了算法的具体实现方法.在10机系统中分别采用了SWN-PSO算法和遗传算法进行了仿真计算.算例结果表明:所提出的算法不仅有利于粒子之间的信息共享,并且可以更快、更准确地收敛到全局最优解,具有一定的实用性.

Application of Small World Network PSO Algorithm in Power System

In terms of valve-point effect of the conventional generators,unit commitment is a complex combination of variables with mixed-type combinatorial optimization problems.Based on small-world network(SWN) model of the neighborhood structure and particle swarm optimization(PSO),by taking with the minimum total consumption of all the units within 24 time periods as the objective function of,the mathematical model of optimal unit was established,which introduced the small world network model,featuring small average shortest path and clustering coefficient large,into PSO neighborhood structure.By taking the output power as the particle position,both SWN-PSO algorithm and genetic algorithm simulation were applied in a 10 unites system.The simulation results show that the proposed algorithm is conducive to information sharing between the particles,but also can converge faster and more accurately to the global optimal solution.